Yair Neuman
Yair Neuman (b. 1968) is a polymath whose expertise is in interdisciplinary research where he draws on diverse disciplines to creatively address real world and academic challenges.
He is the head of the Functor Lab, at BGU.
Prof. Neuman has published numerous papers and academic books and was a visiting scholar/Prof. at M.I.T, University of Toronto, University of Oxford, and Weizmann Institute of Science.
Beyond his academic work, he developed state-of-the-art algorithms for social and cognitive computing, such as those he developed for IARPA, and is currently developing for a DARPA project.
His forthcoming books are "AI for Understanding Context" (Springer), "Mindmatics: A Nexus of Ideas" (Springer), and "AI for Understanding Human Conversations" (Routledge)
He is the head of the Functor Lab, at BGU.
Prof. Neuman has published numerous papers and academic books and was a visiting scholar/Prof. at M.I.T, University of Toronto, University of Oxford, and Weizmann Institute of Science.
Beyond his academic work, he developed state-of-the-art algorithms for social and cognitive computing, such as those he developed for IARPA, and is currently developing for a DARPA project.
His forthcoming books are "AI for Understanding Context" (Springer), "Mindmatics: A Nexus of Ideas" (Springer), and "AI for Understanding Human Conversations" (Routledge)
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Books by Yair Neuman
Drawing on years of R&D and academic publications in top rated journals, the author provides the reader with a simple and deep understanding of context and its modeling for specific challenges, from identifying social norm violations to understanding conversations going awry and stories by great authors. The book may interest a wide variety of readers seeking to incorporate AI into their understanding of human behavior.
The book outlines the affordances of new technologies for textual analysis, which has historically employed established approaches within the humanities. Neuman, Danesi, and Vilenchik argue that these different forms of analysis are indeed complementary, demonstrating the ways in which AI-based perspectives echo similar theoretical and methodological currents in traditional approaches while also offering new directions for research. The volume showcases examples from a wide range of texts, including novels, television shows, and films to illustrate the ways in which the latest AI technologies can be used for "dialoguing" with textual characters and examining textual meaning coherence.
Illuminating the potential of AI language models to both enhance and extend research on the interpretation of texts, this book will appeal to scholars interested in cognitive approaches to the humanities in such fields as literary studies, discourse analysis, media studies, film studies, psychology, and artificial intelligence.
https://www.routledge.com/How-to-Find-a-Needle-in-a-Haystack-From-the-Insider-Threat-to-Solo-Perpetrators/Neuman/p/book/9781032229768#
Searching for a needle in a haystack is an important task in several contexts of data analysis and decision-making. Examples include identifying the insider threat within an organization, the prediction of failure in industrial production, or pinpointing the unique signature of a solo perpetrator, such as a school shooter or a lone wolf terrorist. It is a challenge different from that of identifying a rare event (e.g., a tsunami) or detecting anomalies because the "needle" is not easily distinguished from the haystack. This challenging context is imbued with particular difficulties, from the lack of sufficient data to train a machine learning model through the identification of the relevant features and up to the painful price of false alarms, which might cause us to question the relevance of machine learning solutions even if they perform well according to common performance criteria. In this book, Prof. Neuman approaches the problem of finding the needle by specifically focusing on the human factor, from solo perpetrators to insider threats. Providing for the first time a deep, critical, multidimensional, and methodological analysis of the challenge, the book offers data scientists and decision makers a deep scientific foundational approach combined with a pragmatic practical approach that may guide them in searching for a needle in a haystack.
"Conceptual Mathematics and Literature employs an interdisciplinary mathematical approach that is a uniquely insightful interpretive analysis of literary works. It offers novel ways of comprehending previously unnoticed underlying patterns of meaning. Numerous figures illustrate and reinforce the author's brilliant and illuminating mode of critical inquiry."-Frank Nuessel, Professor of Modern Languages and Linguistics, University Scholar at the University of Louisville
According to the central thesis of biosemiotics, sign processes characterise all living systems and the very nature of life, and their diverse phenomena can be best explained via the dynamics and typology of sign relations. The authors are therefore presenting a deeper view on biological evolution, intentionality of organisms, the role of communication in the living world and the nature of sign systems — all topics which are described in this volume. This has important consequences on the methodology and epistemology of biology and study of life phenomena in general, which the authors aim to help the reader better understand."
Papers by Yair Neuman
Drawing on years of R&D and academic publications in top rated journals, the author provides the reader with a simple and deep understanding of context and its modeling for specific challenges, from identifying social norm violations to understanding conversations going awry and stories by great authors. The book may interest a wide variety of readers seeking to incorporate AI into their understanding of human behavior.
The book outlines the affordances of new technologies for textual analysis, which has historically employed established approaches within the humanities. Neuman, Danesi, and Vilenchik argue that these different forms of analysis are indeed complementary, demonstrating the ways in which AI-based perspectives echo similar theoretical and methodological currents in traditional approaches while also offering new directions for research. The volume showcases examples from a wide range of texts, including novels, television shows, and films to illustrate the ways in which the latest AI technologies can be used for "dialoguing" with textual characters and examining textual meaning coherence.
Illuminating the potential of AI language models to both enhance and extend research on the interpretation of texts, this book will appeal to scholars interested in cognitive approaches to the humanities in such fields as literary studies, discourse analysis, media studies, film studies, psychology, and artificial intelligence.
https://www.routledge.com/How-to-Find-a-Needle-in-a-Haystack-From-the-Insider-Threat-to-Solo-Perpetrators/Neuman/p/book/9781032229768#
Searching for a needle in a haystack is an important task in several contexts of data analysis and decision-making. Examples include identifying the insider threat within an organization, the prediction of failure in industrial production, or pinpointing the unique signature of a solo perpetrator, such as a school shooter or a lone wolf terrorist. It is a challenge different from that of identifying a rare event (e.g., a tsunami) or detecting anomalies because the "needle" is not easily distinguished from the haystack. This challenging context is imbued with particular difficulties, from the lack of sufficient data to train a machine learning model through the identification of the relevant features and up to the painful price of false alarms, which might cause us to question the relevance of machine learning solutions even if they perform well according to common performance criteria. In this book, Prof. Neuman approaches the problem of finding the needle by specifically focusing on the human factor, from solo perpetrators to insider threats. Providing for the first time a deep, critical, multidimensional, and methodological analysis of the challenge, the book offers data scientists and decision makers a deep scientific foundational approach combined with a pragmatic practical approach that may guide them in searching for a needle in a haystack.
"Conceptual Mathematics and Literature employs an interdisciplinary mathematical approach that is a uniquely insightful interpretive analysis of literary works. It offers novel ways of comprehending previously unnoticed underlying patterns of meaning. Numerous figures illustrate and reinforce the author's brilliant and illuminating mode of critical inquiry."-Frank Nuessel, Professor of Modern Languages and Linguistics, University Scholar at the University of Louisville
According to the central thesis of biosemiotics, sign processes characterise all living systems and the very nature of life, and their diverse phenomena can be best explained via the dynamics and typology of sign relations. The authors are therefore presenting a deeper view on biological evolution, intentionality of organisms, the role of communication in the living world and the nature of sign systems — all topics which are described in this volume. This has important consequences on the methodology and epistemology of biology and study of life phenomena in general, which the authors aim to help the reader better understand."
https://arxiv.org/abs/2205.01731
Keywords: complex social systems, Tsallis entropy, word dynamics, historical corpora, multidisciplinary science
* This paper has been submitted for publication
Abstract
The aim of this study was to analyze dynamic patterns for scanning femoroacetabular impingement (FAI) radiographs in orthopedics, in order to better understand the nature of expertise in radiography. Seven orthopedics residents with at least two years of expertise and seven board-certified orthopedists participated in the study. The participants were asked to diagnose 15 anteroposterior (AP) pelvis radiographs of 15 surgical patients, diagnosed with FAI syndrome. Eye tracking data were recorded using the SMI desk-mounted tracker and were analyzed using advanced measures and methodologies, mainly recurrence quantification analysis.
The expert orthopedists presented a less predictable pattern of scanning the radiographs although there was no difference between experts and non-experts in the deterministic nature of their scan path. In addition, the experts presented a higher percentage of correct areas of focus and more quickly made their first comparison between symmetric regions of the pelvis. We contribute to the understanding of experts’ process of diagnosis by showing that experts are qualitatively different from residents in their scanning patterns. The dynamic pattern of scanning that characterizes the experts was found to have a more complex and less predictable signature, meaning that experts’ scanning is simultaneously both structured (i.e. deterministic) and unpredictable.